Many articles of manufacture are distinguished by having exterior packaging assembled from two or more components. A feature often desired by the manufacturer is to have the assembled components have the appearance of being seamless. This is achieved by having the components manufactured to tolerances such that when they are assembled, the seam between components is not readily discernable. Problems with this method of assembly arise when, in spite of the tolerances to which the parts were manufactured, small variations arise in the parts to be mated. One solution to this problem is to inspect the articles after they are assembled to determine whether or not the surfaces have mated with acceptable accuracy.
The simplest solution is to have humans inspecting the finished articles by eye to determine if they are acceptable. While humans are relatively easy to train and replace, difficulties with human inspection include inconsistent results over time and difficulties with making quantitative judgments regarding the articles being inspected. For example, a human can be trained to inspect articles by showing them good and bad articles and can then be expected to accept or reject further articles depending upon how much they resemble the examples shown. Difficulty comes, however in that human inspectors make qualitative, rather than quantitative decisions. This makes it very difficult to inspect parts to specific numerical tolerances, meaning that different inspectors may classify the same part differently. This tends to defeat the purpose of inspecting articles to insure uniform appearance. This is especially difficult when the parts happen to be made of differing materials, such as when a plastic part needs to mate with an aluminum part.
To insure uniform appearance and enforce quantitative standards for quality, machine vision systems can be used to inspect articles. 2D machine vision systems which rely on color or grayscale images have the same difficulties that humans do in quantitatively measuring anomalies in surface mating applications. For this reason 3D vision is sometimes used to inspect surfaces. There are many different methods of extracting 3D information from surfaces, each of which varies with regard to resolution, accuracy and speed. The subset of 3D measurement methods most suited to solving this surface inspection problem involve projecting patterns of light (“structured light”) on a surface and measuring the local displacements of the pattern to extract 3D information about the surface.
Structured light can be created on the surface of an article in several different ways. The methods we are interested in project an in-focus pattern onto the surface and then extract information about the surface by acquiring an image or images of the pattern, measuring the 2D location of portions of the pattern and calculating the height of the point geometrically. A schematic diagram of a prior art structured light system is shown in FIG. 1. Structured light projector 2 projects structured light 4 onto an article 6, where it is imaged by a camera 8 to form an image 10 which is acquired and processed by computer 12. FIG. 2 shows an example of how a prior art structured light system can form an image of a discontinuous edge on an article. In this diagram, a structured light projector 14 projects stripes 16 onto an article 18 which is composed of an upper surface 20 and a lower surface 22. The stripes 16 are projected onto the surfaces 20 and 22 to form the projected patterns 24 and 26. These patterns 24 and 26 are imaged by the camera 28 and subsequently acquired by the computer 30 to form an image 32 to be subsequently processed by the computer 30. The image 32 shows schematically how the discontinuity between surfaces 20 and 22 shows up as a displacement between groups of imaged lines 34 and 36. Computer 30 typically uses the displacement of the structured light to determine the relative altitude of a portion of an article.
FIG. 3 is a schematic diagram illustrating how a prior art structured light system can be used to calculate the relative altitude of points on surfaces based on geometry. In FIG. 3, a structured light projector 40 projects lines, one of which is shown 42. This line extends into and out of the plane of the drawing and impinges an article 52 having two surfaces 44 and 46 much like the article in FIG. 2. Light from the line 62 is reflected from the article 68 is imaged by a camera/lens system (not shown) onto an image sensor plane 54. In this case the image sensor plane 54 would intersect the light 48 reflected from surface 44 at point 56 and light 50 reflected from surface 46 at point 58. Note that the difference in altitude between surfaces 44 and 46, marked as “B” in FIG. 3 results in a difference between points 56 and 58 on the image sensor plane 54 marked as “A” in the image plane. In practice, well known machine vision techniques can be used to determine the location of points 56 and 58 in the image plane and measure the distance A. In this example the distance A is related to the distance B by the equationA=B(1+tan(a)/tan(b))  1)Where a and b are the angles indicated in FIG. 3 as the illumination and viewing angles respectively.
One example of this method is described in U.S. Pat. No. 6,064,759 by inventors B. Shawn Buckley, et al. In this example, a single camera position is used to acquire an image of the structured light projected onto a surface following which geometric models are fit to the surface. Multiple points in the surface image are used to form a single 3D point, which improves the accuracy of the measurement.
A similar method described in “Technique for Phase Measurement and Surface Reconstruction by Use of Colored Structured Light”, by Oleksandr, et al, (Applied Optics Vol. 41, Issue 29, pp 6104-6117 (2002)), projects multiple colored patterns to distinguish the projected light and attempt to improve the accuracy of the 3D measurements. They discuss using structured light to determine the topography of an automotive windshield using differential equations to extract 3D information from the structured light images.
Two problems exist with these methods. The first is speed. Creating a 3D map of an entire surface is time consuming. In the application we are contemplating herein, we are not interested in the entire surface, only the small area adjacent to the seam between components. Prior art methods are aimed at characterizing an entire surface or article, rather than solely extracting information regarding the mating of two surfaces. Thus far more calculations are performed by prior art systems than are desired for this application.
The second problem is illustrated by FIG. 4. In FIG. 4, a structured light projector 60 projects a pattern consisting of lines onto an article 68, one of which is shown 62. The line of light 62 intersects the article 68 at two surfaces 64, 66. The light intersecting the surfaces is imaged by a camera/lens system (not shown) onto the image plane of the sensor 76. Light from the stripe 62 is reflected by the top surface 66 to form light ray 70 which intersects the image sensor plane 76 at point 78. In this example, light reflecting off the bottom surface 64 deviates from the expected direction onto a new direction 74 as a result of a small defect in the surface 72, causing the light ray 74 to intersect the image sensor plane 76 at point 80. This results in a measured distance A when machine vision techniques are used to measure the distance between points 78 and 80 on the image sensor plane. If the calculations shown in equation 1, above, were applied to measurement C with the expectation that distance D could be accurately estimated, an error would occur. In the instant application, we are interested in both detecting small defects in the surfaces and distinguishing them from more systematic differences in surfaces caused by mis-mated surfaces. The methods mentioned above would either filter the localized defects out or fold them into the calculation, making them indistinguishable from systematic differences between mating surfaces. Neither of these results is desirable for the instant application. For these reasons, there is a need for a method for efficiently and accurately detecting mis-mated surfaces using structured light while rejecting false positives caused by localized defects.